Forecast For 2010: Better Forecasting

by Pat LaPointe, Jan 5, 2010, 11:46 AM

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Yogi Berra said, "It's tough to make predictions, especially about the future." Yet the turn of the year (and the decade) make forecasting an irresistible temptation. But what if forecasting is part of your job, not just a hobby? How do make sure your forecasts are smart, relevant, and even (dare I say) accurate?

While advanced mathematics and enormous computational power have improved forecasting potential significantly, few would argue that forecasting is an exact science. That's because at its core, forecasting is still mostly a human dynamic where accuracy is dependent upon...

·       asking the right people the right questions;

·       their willingness to answer truthfully and completely;

·       the ability to separate the meaningful elements from the noise; and,

·       the openness of the forecaster to suggestions of process improvement.

That last point is key: process improvement.

Consistently good forecasting isn't a mathematical exercise performed at regular intervals (e.g., quarterly) as much as it's an ongoing process of gathering and evaluating dozens or hundreds of points of information into a decision framework. Then, when called upon, this decision framework can output the best forward-looking view grounded in the insights of the contributors. While software can facilitate process structure by prompting for specific fields of information to be included, it cannot make judgments on the quality of the information being input. Garbage in; garbage out.

As marketers, our job is to consistently prepare forecasts that help our companies conceive, plan, test, build and ultimately sell successful products and services. Sound forecasting processes form the foundation of an "early warning" system to alert the rest of the organization to the need to rethink its market orientation. In essence, forecasting becomes the rudder that can help your company stay the course, change directions, or navigate uncharted waters with confidence. As such, marketing migrates from being a tactical player to a strategic resource for the CEO when forecasts become more accurate, timely, and reliable.

Five Keys to Better Forecasting

Here are a few things I've learned over the years that result in much better forecasts:

1.     Be Specific. Define exactly what you are trying to forecast. If you say "sales,"what do you really mean? Revenue? Unit volume? Gross margins? Net profit? The differences are substantial and might cause you to take very different approaches to forecasting. Likewise, having some sense of how far out you need to forecast (e.g. 3 months, 12, 36, etc.) and how accurate you need to be will guide you to use forecasting methods and processes better suited to your objectives.

2.     Be Structured. Being methodical in defining all of the dimensions, variables, facts, and assumptions will pay huge dividends in several ways, including explaining your forecast to skeptics and inspiring confidence that you've been comprehensive and credible in your approach.

3.     Be Quantitative - With or Without Data. Regardless of how little data you have, there are scientifically developed and proven ways of making better decisions. You may not have the raw materials for statistical regression forecasting, but you surely can use Delphi techniques of other judgmental calculus tools to transform perceptions and intuition of managers into data sets which can be more fully examined and questioned. Often, the process of quantifying the fuzzier logic uncovers great insights that were previously overlooked.

4.     Triangulate - Use multiple forecasting methods and see how the results differ. Chances are that the "reality" is somewhere within the triangle of results. That level of accuracy may be sufficient. But even if it isn't, the multiple-method approach highlights weaknesses in any single method that might otherwise be overlooked -- and that in itself leads to more accurate forecasts.

5.     KISS - Keep it simple, stupid. As with most things in life, simplicity is a virtue in forecasting. Einstein said that "things should be made as simple as possible, but no simpler." In forecasting,we interpret that to mean that an accurate and reliable forecasting process should be comprehensive enough to identify the truly causal factors, but simple enough to explain to those who will need to make decisions upon it. There is no power in a forecast if those who need to trust it cannot understand or explain the logic and process behind it.

Recognizing forecasting to be a complex human decision process is the first step toward improving the accuracy and reliability of the forecasts coming out of your department.

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0 comments on "Forecast For 2010: Better Forecasting".

1. Eric Head from ForeSee Results
   

   commented on: January 5, 2010 at 4:53 p.m.

   As always Pat, a fantastically relevant post. I would also add this as another complicating factor in coming up w/ accurate forecasts: probably more than half of any given business outcome is due to factors that are outside the company's control - e.g. the competition suddenly launches a new product, the price of a barrel of oil increases suddenly due to political unrest in a foreign country, etc.

   All the more reason, to your point, why forecasting should be an on-going process rather than a periodic point-in-time exercise.
   

2. Jeff Aliotta from Anova Group
   

   commented on: January 5, 2010 at 5:34 p.m.

   As managing partner of a digital management agency that heavily leverages multiple regression analysis, I appreciate the discussion on forecasting within the digital marketing realm. In addition to the suggestions in this article, I would add that the most difficult part of forecasting is identifying the decisions that truly impact your results. For example, were the sales of your #1 product driven by the position of your paid search ads, by the pricing of the product, or because of the content on your website? In forecasting, the first step is to identify the drivers of performance – or in mathematical language – the statistically significant indicators of performance.

   By leveraging multiple regression analysis, most companies would be surprised to understand the drivers of their online performance. For example, we have seen cases where a single word in paid search keywords has statistically significant impact on results.
   

3. Paula Lynn from Who Else Unlimited
   

   commented on: January 5, 2010 at 6:27 p.m.

   This is what I call one for the wall and keep it up there all year. Coming from a sales perspective, how about keeping as realistic as possible. Just because the upper echelons want a certain dollar growth, doesn't mean they will implement what it will take to get there. It can suck the life out of sales.

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